{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f349f5f-96a6-4a73-825f-8bc8190cd004",
   "metadata": {},
   "source": [
    "# 7-segment displays\n",
    "\n",
    "This notebook gives a cute example to show off memo's interoperability with the broader JAX ecosystem, which includes deep learning, graphics, physics simulation, and more.\n",
    "\n",
    "A [seven-segment display](https://en.wikipedia.org/wiki/Seven-segment_display) uses seven lights to display a variety of characters. But when designing a \"font\" for such a display, [you have to be careful to avoid ambiguities](https://harold.thimbleby.net/cv/files/seven-segment.pdf): for example, a capital \"B\" and the letter \"8\" naïvely map to the same pattern (all seven segments on), but are impossible to distinguish.\n",
    "\n",
    "Can we model intuitions about 7-segment displays as a kind of rational communication? To do so, let us start by training a simple neural network model of hand-written character recognition on the EMNIST dataset, using JAX's `flax` ecosystem for deep learning. We will then build a memo model that reasons _over_ this neural network!\n",
    "\n",
    "We'll start with some imports…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e58e74e-e5b4-42a5-b651-e2035f63f017",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "\n",
    "import torchvision.datasets\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from memo import memo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f68f62-ad3b-408c-8956-72770beec4bc",
   "metadata": {},
   "source": [
    "Next, we use PyTorch's built-in tools to download and parse the EMNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06ed0ff3-2413-4d39-b5bb-2b82653c434f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_letter = torchvision.datasets.EMNIST(root='7-aux/data', split='letters', download='True')\n",
    "ds_number = torchvision.datasets.EMNIST(root='7-aux/data', split='digits', download='True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1b290fd8-71f6-4245-8ea9-d5a6f7c09412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d78a756a45e14437a8f02a912cfd060e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/240000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cd0aa0c91af4121b4dcc383e91e0e9d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/124800 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feats = []\n",
    "labls = []\n",
    "\n",
    "from collections import Counter\n",
    "cnt = Counter()\n",
    "for feat, labl in tqdm(ds_number):\n",
    "    if cnt[labl] > 4800:\n",
    "        continue\n",
    "    cnt[labl] += 1\n",
    "    feats.append(np.array(feat).T)\n",
    "    labls.append(labl)\n",
    "for feat, labl in tqdm(ds_letter):\n",
    "    if 1 <= labl <= 26:\n",
    "        feats.append(np.array(feat).T)\n",
    "        labls.append(labl + 10 - 1)\n",
    "\n",
    "NUM_CLASS = 36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bca460e4-034f-42bd-ba69-117de786524a",
   "metadata": {},
   "outputs": [],
   "source": [
    "feats_ = np.expand_dims(np.array(feats), -1)\n",
    "labls_ = np.expand_dims(np.array(labls), -1)\n",
    "ds = list(zip(feats_, labls_))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9188578-12cb-45e6-a9e0-ea9221fefe0f",
   "metadata": {},
   "source": [
    "Next, we train a tiny neural network on this dataset. (Or, we load saved weights from a file if a checkpoint exists.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "476f82b6-c801-43f6-895f-964b1ec106a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from flax import linen as nn\n",
    "from flax.training import train_state\n",
    "import optax\n",
    "\n",
    "import orbax.checkpoint\n",
    "from flax.training import orbax_utils\n",
    "import os.path\n",
    "\n",
    "\n",
    "class CNN(nn.Module):\n",
    "    @nn.compact\n",
    "    def __call__(self, x):\n",
    "        x = x / 255\n",
    "        # x = nn.Conv(features=32, kernel_size=(3, 3))(x)\n",
    "        # x = nn.relu(x)\n",
    "        # x = nn.avg_pool(x, window_shape=(2, 2), strides=(1, 1))\n",
    "        # x = nn.Conv(features=64, kernel_size=(3, 3))(x)\n",
    "        # x = nn.relu(x)\n",
    "        # x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2))\n",
    "        x = x.reshape((x.shape[0], -1))  # flatten\n",
    "        x = nn.Dense(features=256)(x)\n",
    "        x = nn.relu(x)\n",
    "        x = nn.Dense(features=256)(x)\n",
    "        x = nn.relu(x)\n",
    "        x = nn.Dense(features=NUM_CLASS)(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "@jax.jit\n",
    "def apply_model(state, images, labels):\n",
    "    # print(images.shape, labels.shape)\n",
    "    def loss_fn(params):\n",
    "        logits = state.apply_fn({'params': params}, images)\n",
    "        one_hot = jax.nn.one_hot(labels, NUM_CLASS)[:, 0, :]\n",
    "        loss = jnp.mean(optax.softmax_cross_entropy(logits=logits, labels=one_hot))\n",
    "        return loss, logits\n",
    "\n",
    "    grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n",
    "    (loss, logits), grads = grad_fn(state.params)\n",
    "    accuracy = jnp.mean(jnp.argmax(logits, -1) == labels[:, 0])\n",
    "    print((jnp.argmax(logits, -1) == labels[:, 0]).shape)\n",
    "    return grads, loss, accuracy\n",
    "\n",
    "\n",
    "@jax.jit\n",
    "def update_model(state, grads):\n",
    "    return state.apply_gradients(grads=grads)\n",
    "\n",
    "\n",
    "rng = jax.random.key(0)\n",
    "rng, _ = jax.random.split(rng)\n",
    "cnn = CNN()\n",
    "params = cnn.init(rng, jnp.ones([1, 28, 28, 1]))['params']\n",
    "tx = optax.adam(0.0001)\n",
    "state = train_state.TrainState.create(apply_fn=cnn.apply, params=params, tx=tx)\n",
    "\n",
    "if os.path.exists('./7-aux/weights'):\n",
    "    orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n",
    "    params = orbax_checkpointer.restore(os.path.abspath('./7-aux/weights'))['state']['params']\n",
    "    state = train_state.TrainState.create(apply_fn=cnn.apply, params=params, tx=tx)\n",
    "else:\n",
    "    losses = []\n",
    "    accs = []\n",
    "    for epoch in tqdm(range(50)):\n",
    "        from torch.utils.data import DataLoader\n",
    "        dl = DataLoader(ds, batch_size=128, shuffle=True)\n",
    "        for t, (feat, labl) in enumerate((dl)):\n",
    "            feat = jnp.array(feat)\n",
    "            labl = jnp.array(labl)\n",
    "            grads, loss, accuracy = apply_model(state, feat, labl)\n",
    "            state = update_model(state, grads)\n",
    "            if t % 400 == 0:\n",
    "                losses.append(loss)\n",
    "                accs.append(accuracy)\n",
    "        # print(epoch, loss, accuracy)\n",
    "\n",
    "    plt.plot(accs)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Time')\n",
    "\n",
    "    ckpt = {'state': state}\n",
    "    orbax_checkpointer = orbax.checkpoint.PyTreeCheckpointer()\n",
    "    save_args = orbax_utils.save_args_from_target(ckpt)\n",
    "    orbax_checkpointer.save(os.path.abspath('./7-aux/weights'), ckpt, save_args=save_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d8ea964-4faf-42de-b0a8-6759a831aa54",
   "metadata": {},
   "source": [
    "Now, let's feed our trained neural network some 7-segment displays and see how it does. We can generate 7-segment displays in JAX by performing simple pixel manipulations on an array.\n",
    "\n",
    "Do the neural network's guesses match your intuitions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ff20d05-a5f2-4555-937b-5d58e4818da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 128 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@jax.jit\n",
    "def predict(img):\n",
    "    return state.apply_fn(\n",
    "        {'params': state.params},\n",
    "        jnp.expand_dims(img, (0, -1))\n",
    "    )[0]\n",
    "\n",
    "@jax.jit\n",
    "def make_7seg(i):\n",
    "    L = +9\n",
    "    R = -11\n",
    "    U = 4\n",
    "    M = 13\n",
    "    D = 22\n",
    "    canvas = jnp.zeros((28, 28))\n",
    "    segs = [(i & (1 << n)) != 0 for n in range(7)]\n",
    "    canvas = canvas.at[ U, L:R].set(segs[0])\n",
    "    canvas = canvas.at[ M, L:R].set(segs[1])\n",
    "    canvas = canvas.at[ D, L:R].set(segs[2])\n",
    "    canvas = canvas.at[ U+1:M, L-1].set(segs[3])\n",
    "    canvas = canvas.at[ U+1:M, R].set(segs[4])\n",
    "    canvas = canvas.at[ M+1:D, L-1].set(segs[5])\n",
    "    canvas = canvas.at[ M+1:D, R].set(segs[6])\n",
    "    canvas = (\n",
    "        canvas\n",
    "        + jnp.roll(canvas, 1, axis=0)\n",
    "        + jnp.roll(canvas, 1, axis=1)\n",
    "        + jnp.roll(canvas, 2, axis=0)\n",
    "        + jnp.roll(canvas, 2, axis=1)\n",
    "        + jnp.roll(jnp.roll(canvas, 1, axis=0), 1, axis=1)\n",
    "    )\n",
    "\n",
    "    canvas = 255 * (canvas != 0)\n",
    "    return canvas\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(128):\n",
    "    plt.subplot(8, 16, i + 1)\n",
    "    img = np.array(make_7seg(i))\n",
    "    plt.imshow(img)\n",
    "    import string\n",
    "    chars = string.digits + string.ascii_uppercase\n",
    "    name = (\n",
    "        chars[predict(img).argsort()[-1]]\n",
    "        # + ', ' +\n",
    "        # chars[predict(img).argsort()[-2]]\n",
    "    )\n",
    "    plt.title(name)\n",
    "    plt.axis('off')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df5fe32-9a82-4988-9349-fcd0c958bb50",
   "metadata": {},
   "source": [
    "Finally, let's build an RSA model where the literal listener is modeled as running our neural network on the input image (\"utterance\") and taking the logits as the log-posterior. Notice that the utterance space is 128, i.e. $2^7$ configurations of the seven-segment display, and the referent space is 36, i.e. 10 digits + 26 letters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5480a83d-9364-4ec9-b550-1e85228dcf11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from memo import memo\n",
    "\n",
    "U = jnp.arange(128)\n",
    "R = jnp.arange(36)\n",
    "\n",
    "@jax.jit\n",
    "def pretrained_classifier(u, r):\n",
    "    return predict(make_7seg(u))[r]\n",
    "\n",
    "@memo\n",
    "def literal_speaker[u: U, r: R]():\n",
    "    cast: [speaker]\n",
    "    speaker: knows(r)\n",
    "    speaker: chooses(u in U, wpp=exp(pretrained_classifier(u, r)))\n",
    "    return E[speaker.u == u]\n",
    "\n",
    "@memo\n",
    "def speaker[u: U, r: R](beta, t):\n",
    "    cast: [speaker, listener]\n",
    "    speaker: knows(r)\n",
    "    speaker: chooses(u in U, wpp=imagine[\n",
    "        listener: knows(u),\n",
    "        listener: chooses(\n",
    "            r in R,\n",
    "            wpp=listener[u, r](beta, t) if t > 0 else exp(pretrained_classifier(u, r))\n",
    "        ),\n",
    "        exp(beta * E[listener.r == r])\n",
    "    ])\n",
    "    return E[speaker.u == u]\n",
    "\n",
    "@memo\n",
    "def listener[u: U, r: R](beta, t):\n",
    "    cast: [listener, speaker]\n",
    "    listener: thinks[\n",
    "        speaker: given(r in R, wpp=1),\n",
    "        speaker: chooses(\n",
    "            u in U,\n",
    "            wpp=speaker[u, r](beta, t - 1),\n",
    "        )\n",
    "    ]\n",
    "    listener: observes [speaker.u] is u\n",
    "    listener: chooses(r in R, wpp=E[speaker.r == r])\n",
    "    return E[listener.r == r]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44eef2d5-b809-4ed9-b20b-e1462f9df41b",
   "metadata": {},
   "source": [
    "Now, we can see how the emergent convention / \"font\" changes as we add more levels of recursive reasoning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be24d887-94f0-470e-a3dc-d067141ff041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 278 ms, sys: 20 ms, total: 298 ms\n",
      "Wall time: 245 ms\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 278 ms, sys: 12.2 ms, total: 290 ms\n",
      "Wall time: 242 ms\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxUAAAGMCAYAAACs64+oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABe4klEQVR4nO3dd3gUZdcG8Ht2N23TSIUkpFMCoYg0BYRIByEKUqUXBQUsn6CvFUWxgFhRBFFQpDcNKIoFpCNFQHoICUIgISEkJNkk2+b7wzd5CRtKdjY7s7v377pySWZ2nj05nilnp6wgiqIIIiIiIiIiK6nkDoCIiIiIiBwbmwoiIiIiIpKETQUREREREUnCpoKIiIiIiCRhU0FERERERJKwqSAiIiIiIknYVBARERERkSRsKoiIiIiISBI2FUREREREJAmbCiIiGWVkZEAQBCxevFhRYzlTLHKKiYlBnz595A6DiKjGsakgIkX5+++/MWDAAERHR8PT0xMRERHo1q0bPvnkk0qve+utt/Ddd9/VSAy7du3Ca6+9hvz8/BoZnyqbOXMmkpOTUbt2bQiCgNdee03ukIiIqJrYVBCRYuzatQutWrXC4cOH8eijj2Lu3LkYP348VCoVPvroo0qvremm4vXXX7dLUxEdHY2SkhKMGDGixt9LqV5++WXs27cPLVq0kDsUIiKykkbuAIiIys2cORP+/v7Yt28fatWqVWne5cuXrR63uLgY3t7eEqOrGYIgwNPTU+4wZJWeno6YmBjk5uYiJCRE7nAcipJrm4hcC89UEJFipKWlITEx0aKhAIDQ0NCKfwuCgOLiYnz99dcQBAGCIGD06NEAgNdeew2CIOD48eN45JFHEBAQgA4dOgAAjhw5gtGjRyMuLg6enp6oU6cOxo4diytXrlSM/dprr2HatGkAgNjY2IrxMzIyKl7z7bffomXLlvDy8kJgYCCGDBmC8+fPW8T86aefIi4uDl5eXmjTpg22b9+OpKQkJCUlVbzmZvcenDx5EoMGDUJISAi8vLzQsGFDvPTSS9XM6P/GGjBgAAIDA+Hp6YlWrVohJSWlYv7+/fshCAK+/vpri2V//vlnCIKAjRs3VkzLzMzE2LFjUbt2bXh4eCAxMRFfffWVVbEB/953YK3CwkI8/fTTiImJgYeHB0JDQ9GtWzccPHiw4jVJSUlo0qQJDhw4gHbt2sHLywuxsbH4/PPPLcYrKyvD9OnTUa9ePXh4eCAyMhLPPfccysrKKr1u0aJF6Ny5M0JDQ+Hh4YHGjRtj3rx5dxTz119/DY1GU1FnALB371707NkT/v7+0Gq16NSpE3bu3FlpuVvVNhGR3HimgogUIzo6Grt378bRo0fRpEmTm75uyZIlGD9+PNq0aYPHHnsMABAfH1/pNQMHDkT9+vXx1ltvQRRFAMAvv/yCs2fPYsyYMahTpw6OHTuGBQsW4NixY9izZw8EQUD//v1x+vRpLF++HB988AGCg4MBoOIT9JkzZ+KVV17BoEGDMH78eOTk5OCTTz5Bx44d8ddff1U0RPPmzcPkyZNx33334ZlnnkFGRgYeeughBAQEoG7durfMw5EjR3DffffBzc0Njz32GGJiYpCWloYNGzZg5syZ1crpsWPH0L59e0REROA///kPvL29sWrVKjz00ENYu3Yt+vXrh1atWiEuLg6rVq3CqFGjKi2/cuVKBAQEoEePHgCA7Oxs3HPPPRAEAZMnT0ZISAg2bdqEcePG4dq1a3j66aerFZ9UEydOxJo1azB58mQ0btwYV65cwY4dO3DixAncfffdFa+7evUqevfujUGDBmHo0KFYtWoVHn/8cbi7u2Ps2LEAALPZjOTkZOzYsQOPPfYYGjVqhL///hsffPABTp8+Xelyu3nz5iExMRHJycnQaDTYsGEDnnjiCZjNZkyaNOmm8S5YsAATJ07Eiy++iDfffBMA8Pvvv6NXr15o2bIlpk+fDpVKVdG0bN++HW3atKk0RlW1TUQkO5GISCE2b94sqtVqUa1Wi/fee6/43HPPiT///LOo1+stXuvt7S2OGjXKYvr06dNFAOLQoUMt5ul0Ootpy5cvFwGI27Ztq5g2e/ZsEYCYnp5e6bUZGRmiWq0WZ86cWWn633//LWo0morpZWVlYlBQkNi6dWvRYDBUvG7x4sUiALFTp04V09LT00UA4qJFiyqmdezYUfT19RXPnTtX6X3MZrNF/NeraqwuXbqITZs2FUtLSyuN065dO7F+/foV01544QXRzc1NzMvLq5hWVlYm1qpVSxw7dmzFtHHjxolhYWFibm5upfceMmSI6O/vX5HjqmK5nZycHBGAOH369Dtext/fX5w0adItX9OpUycRgDhnzpyKaWVlZeJdd90lhoaGVtTXkiVLRJVKJW7fvr3S8p9//rkIQNy5c2fFtKpqqUePHmJcXFyladHR0eIDDzwgiqIofvTRR6IgCOIbb7xRMd9sNov169cXe/ToUen/r06nE2NjY8Vu3bpVTLtVbRMRyY2XPxGRYnTr1g27d+9GcnIyDh8+jFmzZqFHjx6IiIiodLnOnZg4caLFNC8vr4p/l5aWIjc3F/fccw8AVLpc5mbWrVsHs9mMQYMGITc3t+KnTp06qF+/PrZs2QLg38uJrly5gkcffRQazf9OCA8bNgwBAQG3fI+cnBxs27YNY8eORVRUVKV5giDcNsbr5eXl4ffff8egQYNQWFhYEe+VK1fQo0cPpKamIjMzEwAwePBgGAwGrFu3rmL5zZs3Iz8/H4MHDwYAiKKItWvXom/fvhBFsVIOevTogYKCgjvKoy3VqlULe/fuxcWLF2/5Oo1GgwkTJlT87u7ujgkTJuDy5cs4cOAAAGD16tVo1KgREhISKv1tnTt3BoCK/79A5VoqKChAbm4uOnXqhLNnz6KgoMDi/WfNmoWnnnoK7777Ll5++eWK6YcOHUJqaioeeeQRXLlypeI9i4uL0aVLF2zbtg1ms7nSWFXVNhGR3Hj5ExEpSuvWrbFu3Tro9XocPnwY69evxwcffIABAwbg0KFDaNy48R2NExsbazEtLy8Pr7/+OlasWGFx43dVB4I3Sk1NhSiKqF+/fpXz3dzcAADnzp0DANSrV6/SfI1Gc9v7B86ePQsAt7z8606dOXMGoijilVdewSuvvFLlay5fvoyIiAg0b94cCQkJWLlyJcaNGwfg30ufgoODKw6qc3JykJ+fjwULFmDBggU3Hc+eZs2ahVGjRiEyMhItW7ZE7969MXLkSMTFxVV6XXh4uMUNzQ0aNADw730t99xzD1JTU3HixImb3ix+/d+2c+dOTJ8+Hbt374ZOp6v0uoKCAvj7+1f8/scff+CHH37A888/X+k+CuDfmgJgcdnZjeNd34xWVdtERHJjU0FEiuTu7o7WrVujdevWaNCgAcaMGYPVq1dj+vTpd7T89Z8klxs0aBB27dqFadOm4a677oKPjw/MZjN69uxp8WlwVcxmMwRBwKZNm6BWqy3m+/j43FFs9lL+N02dOrXinogbXd/4DB48GDNnzkRubi58fX2RkpKCoUOHVpxtKR9v+PDhNz0IbtasmS3/hNsaNGgQ7rvvPqxfvx6bN2/G7Nmz8e6772LdunXo1atXtcYym81o2rQp3n///SrnR0ZGAvj3gQJdunRBQkIC3n//fURGRsLd3R0//vgjPvjgA4taSkxMRH5+PpYsWYIJEyZUagrKXzt79mzcddddVb7vjXVVVW0TEcmNTQURKV6rVq0AAJcuXaqYVt1Lga5evYrffvsNr7/+Ol599dWK6eWfFF/vZmPHx8dDFEXExsZWfMpdlejoaAD/nim4//77K6YbjUZkZGTc8sC7/BP2o0eP3voPugPlY7m5uaFr1663ff3gwYPx+uuvY+3atahduzauXbuGIUOGVMwPCQmBr68vTCbTHY1nL2FhYXjiiSfwxBNP4PLly7j77rsxc+bMSk3FxYsXLR6/evr0aQD/e/pUfHw8Dh8+jC5dutyyvjZs2ICysjKkpKRUukTt+sujrhccHIw1a9agQ4cO6NKlC3bs2IHw8PCK9wQAPz8/ReWUiKi6eE8FESnGli1bqnyazY8//ggAaNiwYcU0b2/van05XfmZhRvH//DDDy1eW37geeP4/fv3h1qtxuuvv24xjiiKFY+mbdWqFYKCgvDFF1/AaDRWvGbp0qW4evXqLeMMCQlBx44d8dVXX+Gff/6xeI/qCA0NRVJSEubPn1+pISuXk5NT6fdGjRqhadOmWLlyJVauXImwsDB07NixYr5arcbDDz+MtWvXVtn03DheTTOZTBaXrYWGhiI8PNziEbBGoxHz58+v+F2v12P+/PkICQlBy5YtAfx71iMzMxNffPGFxXuVlJSguLgYQNW1VFBQgEWLFt001rp16+LXX39FSUkJunXrVlErLVu2RHx8PN577z0UFRVZLGfvnBIRWYtnKohIMaZMmQKdTod+/fohISEBer0eu3btwsqVKxETE4MxY8ZUvLZly5b49ddf8f777yM8PByxsbFo27btTcf28/NDx44dMWvWLBgMBkRERGDz5s1IT0+3eG35QeZLL72EIUOGwM3NDX379kV8fDzefPNNvPDCCxWPiPX19UV6ejrWr1+Pxx57DFOnToW7uztee+01TJkyBZ07d8agQYOQkZGBxYsXIz4+/rZnWT7++GN06NABd999Nx577DHExsYiIyMDP/zwAw4dOlStnH766afo0KEDmjZtikcffRRxcXHIzs7G7t27ceHCBRw+fLjS6wcPHoxXX30Vnp6eGDduHFSqyp89vfPOO9iyZQvatm2LRx99FI0bN0ZeXh4OHjyIX3/9FXl5edWKD/j3EcHnzp2ruDdh27ZtFY9bHTFiRMWZnxsVFhaibt26GDBgAJo3bw4fHx/8+uuv2LdvH+bMmVPpteHh4Xj33XeRkZGBBg0aYOXKlTh06BAWLFhQcS/MiBEjsGrVKkycOBFbtmxB+/btYTKZcPLkSaxatQo///wzWrVqhe7du8Pd3R19+/bFhAkTUFRUhC+++AKhoaFVNm/l6tWrh82bNyMpKQk9evTA77//Dj8/PyxcuBC9evVCYmIixowZg4iICGRmZmLLli3w8/PDhg0bqp1TIiK7k+mpU0REFjZt2iSOHTtWTEhIEH18fER3d3exXr164pQpU8Ts7OxKrz158qTYsWNH0cvLSwRQ8XjZ8sdu5uTkWIx/4cIFsV+/fmKtWrVEf39/ceDAgeLFixerfIzpG2+8IUZERIgqlcri8bJr164VO3ToIHp7e4ve3t5iQkKCOGnSJPHUqVOVxvj444/F6Oho0cPDQ2zTpo24c+dOsWXLlmLPnj0rXnOzR68ePXq0IlZPT0+xYcOG4iuvvHLL/N1srLS0NHHkyJFinTp1RDc3NzEiIkLs06ePuGbNGosxUlNTRQAiAHHHjh1Vvk92drY4adIkMTIyUnRzcxPr1KkjdunSRVywYMFtY6lK+SNfq/rZsmXLTZcrKysTp02bJjZv3lz09fUVvb29xebNm4ufffaZxfiJiYni/v37xXvvvVf09PQUo6Ojxblz51qMqdfrxXfffVdMTEwUPTw8xICAALFly5bi66+/LhYUFFS8LiUlRWzWrJno6ekpxsTEiO+++6741VdfWdTK9Y+ULbd3717R19dX7NixY8Wjaf/66y+xf//+YlBQkOjh4SFGR0eLgwYNEn/77beK5W5V20REchNEkd+cQ0RkD2azGSEhIejfv3+Vl9hQzUhKSkJubq5N7lMhIqKq8Z4KIqIaUFpaanEPxDfffIO8vDwkJSXJExQREVEN4T0VREQ1YM+ePXjmmWcwcOBABAUF4eDBg/jyyy/RpEkTDBw4UO7wiIiIbIpNBRFRDYiJiUFkZCQ+/vhj5OXlITAwECNHjsQ777wDd3d3ucMjIiKyKd5TQUREREREkvCeCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkrCpICIiIiIiSdhUEBERERGRJGwqiIiIiIhIEjYVREREREQkCZsKIiIiIiKShE0FERERERFJwqaCiIiIiIgkYVNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSRTcVZWVleP755xEeHg4vLy+0bdsWv/zyi9xhKV5RURGmT5+Onj17IjAwEIIgYPHixXKHpXj79u3D5MmTkZiYCG9vb0RFRWHQoEE4ffq03KEp2rFjxzBw4EDExcVBq9UiODgYHTt2xIYNG+QOzeHMnDkTgiCgSZMmcoeiaFu3boUgCFX+7NmzR+7wHMLBgweRnJyMwMBAaLVaNGnSBB9//LHcYSnW6NGjb1pzgiAgMzNT7hAVLTU1FUOGDEHdunWh1WqRkJCAGTNmQKfTyR2aoh04cAA9e/aEn58ffH190b17dxw6dEjusG5KI3cAtzJ69GisWbMGTz/9NOrXr4/Fixejd+/e2LJlCzp06CB3eIqVm5uLGTNmICoqCs2bN8fWrVvlDskhvPvuu9i5cycGDhyIZs2aISsrC3PnzsXdd9+NPXv28EDvJs6dO4fCwkKMGjUK4eHh0Ol0WLt2LZKTkzF//nw89thjcofoEC5cuIC33noL3t7ecofiMJ588km0bt260rR69erJFI3j2Lx5M/r27YsWLVrglVdegY+PD9LS0nDhwgW5Q1OsCRMmoGvXrpWmiaKIiRMnIiYmBhERETJFpnznz59HmzZt4O/vj8mTJyMwMBC7d+/G9OnTceDAAXz//fdyh6hIBw8eRIcOHRAZGYnp06fDbDbjs88+Q6dOnfDnn3+iYcOGcodoSVSovXv3igDE2bNnV0wrKSkR4+PjxXvvvVfGyJSvtLRUvHTpkiiKorhv3z4RgLho0SJ5g3IAO3fuFMvKyipNO336tOjh4SEOGzZMpqgck9FoFJs3by42bNhQ7lAcxuDBg8XOnTuLnTp1EhMTE+UOR9G2bNkiAhBXr14tdygOp6CgQKxdu7bYr18/0WQyyR2OQ9u+fbsIQJw5c6bcoSjazJkzRQDi0aNHK00fOXKkCEDMy8uTKTJl6927txgQECDm5uZWTLt48aLo4+Mj9u/fX8bIbk6xlz+tWbMGarW60qecnp6eGDduHHbv3o3z58/LGJ2yeXh4oE6dOnKH4XDatWsHd3f3StPq16+PxMREnDhxQqaoHJNarUZkZCTy8/PlDsUhbNu2DWvWrMGHH34odygOp7CwEEajUe4wHMayZcuQnZ2NmTNnQqVSobi4GGazWe6wHNKyZcsgCAIeeeQRuUNRtGvXrgEAateuXWl6WFgYVCqVxX6X/rV9+3Z07doVQUFBFdPCwsLQqVMnbNy4EUVFRTJGVzXFNhV//fUXGjRoAD8/v0rT27RpAwCKvqaMnIcoisjOzkZwcLDcoShecXExcnNzkZaWhg8++ACbNm1Cly5d5A5L8UwmE6ZMmYLx48ejadOmcofjUMaMGQM/Pz94enri/vvvx/79++UOSfF+/fVX+Pn5ITMzEw0bNoSPjw/8/Pzw+OOPo7S0VO7wHIbBYMCqVavQrl07xMTEyB2OoiUlJQEAxo0bh0OHDuH8+fNYuXIl5s2bhyeffJKXfN5EWVkZvLy8LKZrtVro9XocPXpUhqhuTbH3VFy6dAlhYWEW08unXbx40d4hkQtaunQpMjMzMWPGDLlDUbxnn30W8+fPBwCoVCr0798fc+fOlTkq5fv8889x7tw5/Prrr3KH4jDc3d3x8MMPo3fv3ggODsbx48fx3nvv4b777sOuXbvQokULuUNUrNTUVBiNRjz44IMYN24c3n77bWzduhWffPIJ8vPzsXz5crlDdAg///wzrly5gmHDhskdiuL17NkTb7zxBt566y2kpKRUTH/ppZfw5ptvyhiZsjVs2BB79uyByWSCWq0GAOj1euzduxcAFPlwAMU2FSUlJfDw8LCY7unpWTGfqCadPHkSkyZNwr333otRo0bJHY7iPf300xgwYAAuXryIVatWwWQyQa/Xyx2Wol25cgWvvvoqXnnlFYSEhMgdjsNo164d2rVrV/F7cnIyBgwYgGbNmuGFF17ATz/9JGN0ylZUVASdToeJEydWPO2pf//+0Ov1mD9/PmbMmIH69evLHKXyLVu2DG5ubhg0aJDcoTiEmJgYdOzYEQ8//DCCgoLwww8/4K233kKdOnUwefJkucNTpCeeeAKPP/44xo0bh+eeew5msxlvvvkmLl26BECZx8GKvfzJy8sLZWVlFtPLT89WdUqIyFaysrLwwAMPwN/fv+L+Hrq1hIQEdO3aFSNHjqy43rNv374QRVHu0BTr5ZdfRmBgIKZMmSJ3KA6vXr16ePDBB7FlyxaYTCa5w1Gs8n3n0KFDK00vvy9g9+7ddo/J0RQVFeH7779Hjx49Kl3vTlVbsWIFHnvsMSxcuBCPPvoo+vfvjy+//BKjRo3C888/jytXrsgdoiJNnDgRL774IpYtW4bExEQ0bdoUaWlpeO655wAAPj4+MkdoSbFNRVhYWEU3dr3yaeHh4fYOiVxEQUEBevXqhfz8fPz000+sNSsNGDAA+/bt4/d83ERqaioWLFiAJ598EhcvXkRGRgYyMjJQWloKg8GAjIwM5OXlyR2mQ4mMjIRer0dxcbHcoShW+fbsxptmQ0NDAQBXr161e0yO5rvvvoNOp+OlT3fos88+Q4sWLVC3bt1K05OTk6HT6fDXX3/JFJnyzZw5E9nZ2di+fTuOHDmCffv2VTxYoUGDBjJHZ0mxTcVdd92F06dPVzw1oFz5tWR33XWXDFGRsystLUXfvn1x+vRpbNy4EY0bN5Y7JIdVfmq2oKBA5kiUKTMzE2azGU8++SRiY2Mrfvbu3YvTp08jNjaW9/JU09mzZ+Hp6anIT/CUomXLlgAsr8cuv0+Rl+Hd3tKlS+Hj44Pk5GS5Q3EI2dnZVZ49NBgMAMCnt91GQEAAOnToUPEgj19//RV169ZFQkKCzJFZUmxTMWDAAJhMJixYsKBiWllZGRYtWoS2bdsiMjJSxujIGZlMJgwePBi7d+/G6tWrce+998odkkO4fPmyxTSDwYBvvvkGXl5ebMxuokmTJli/fr3FT2JiIqKiorB+/XqMGzdO7jAVKScnx2La4cOHkZKSgu7du0OlUuyuTXbl9wB8+eWXlaYvXLgQGo2m4kk9VLWcnBz8+uuv6NevH7RardzhOIQGDRrgr7/+sjhrvXz5cqhUKjRr1kymyBzPypUrsW/fPjz99NOK3M4p9kbttm3bYuDAgXjhhRdw+fJl1KtXD19//TUyMjIsNoZkae7cucjPz6/49GnDhg0V35Y6ZcoU+Pv7yxmeIj377LNISUlB3759kZeXh2+//bbS/OHDh8sUmbJNmDAB165dQ8eOHREREYGsrCwsXboUJ0+exJw5c/ip8U0EBwfjoYcesphe/l0VVc2jfw0ePBheXl5o164dQkNDcfz4cSxYsABarRbvvPOO3OEpWosWLTB27Fh89dVXMBqN6NSpE7Zu3YrVq1fjhRde4OWet7Fy5UoYjUZe+lQN06ZNw6ZNm3Dfffdh8uTJCAoKwsaNG7Fp0yaMHz+eNXcT27Ztw4wZM9C9e3cEBQVhz549WLRoEXr27ImnnnpK7vCqJve3791KSUmJOHXqVLFOnTqih4eH2Lp1a/Gnn36SOyyHEB0dLQKo8ic9PV3u8BSpU6dON82ZwlcVWS1fvlzs2rWrWLt2bVGj0YgBAQFi165dxe+//17u0BwSv1H79j766COxTZs2YmBgoKjRaMSwsDBx+PDhYmpqqtyhOQS9Xi++9tprYnR0tOjm5ibWq1dP/OCDD+QOyyHcc889YmhoqGg0GuUOxaHs3btX7NWrl1inTh3Rzc1NbNCggThz5kzRYDDIHZpinTlzRuzevbsYHBwsenh4iAkJCeLbb78tlpWVyR3aTQmiyEezEBERERGR9ZR3QRYRERERETkUNhVERERERCQJmwoiIiIiIpKETQUREREREUnCpoKIiIiIiCRhU0FERERERJLc8ZffdVMNrMk4FO8X82qrl2XurMsd88aasxZrzjqsOeux5qzDmrMea846rDnr3S53PFNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpLkjh8pa2vqoEAgOBBQCbYd2CwCuXkwXcmz7bjk8Fhz1mPuyN5Yc9Zh3qzH3JG9OVvNydZUZA9oiOhhZ+CpNtp03FKTBv8saYigL3bbdFxyfKw56zF3ZG+sOeswb9Zj7sjenK3m7N5UCBoNBI0GhbHAt3E/QKtyt+n4ReZStIx5BiGenhCNRohG2/6PkpOg0QBqdZXzRL0eEEU7R+RYiqKAb+I2wEfladtxnbjmyjF31WPzdVUQILjfZFtpMjlN3oCa3UfozHrkmfXoFv4cgmw2qjIwb9bjcQnZm7PWnF2bCpW3Ny6NbY6C5np0bXoEbkLVO10pPAQ3tOtyFFtrN0Gtv9xRZ9EhmHU6m7+PvV2fuxup8zWo/+01mA8dlyEyctaaswdnzF1NrKvC3Y1x5hFfmPwtdwzOkjegZvcRJ/Q69NkxCe6pXojYVmqzcZWAebMej0vI3py55uzaVAhaLVTdriC91cr/TrF9It0ENRZFbQeitqNZ8FAIK70AJ1h5LXP3PynFWszaPQLeh+wfFzlvzdmDM+auJtbVwjgfzH1wEXpqyyzmOUvegJrdR5w1BiJsvTu81+6y2ZhKwbxZj8clZG/OXHOy3VNBRER3SLDxTXwuZFsp8OLp/si8GIgGmSVyh+MwmDciqi42FURE5LQWZCXB5xVvNDqTBnNhIXjn2Z1h3oiouhTTVBSZS/F9cQTSy0KqtVysRw4e9M60+Q2k5Hw8cwS8ldMGWrXlte7V4Yo1x9yR3KzdR+xJj0XDy7kwXr1aQ5EpG/NmPR6XkL05es0ppqk4qPfEOwsHI2xX9a75WtFBi5gJc9Ge6y7dRt0157D/zxaSLyVxxZpj7khu1u4j6heUwJR1uYaiUj7mzXo8LiF7c/SaU0xTkW/Swv+sCcLOQ9Vazj/iHhSavQBY3sBIdD3jhUwIFzIlj+OKNcfckdys3UeYayYch8G8WY/HJWRvjl5zKlnfnYiIiIiIHB6bCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkrCpICIiIiIiSdhUEBERERGRJGwqiIiIiIhIEjYVREREREQkCZsKIiIiIiKShE0FERERERFJopE7gG2lwIun+yPzYiAaZJZIHs8gmvB6zl1IyWgC094AiGUXbBClMtk6d2Qdl6i5e5rhcksfiBI/hihobESkJh+AFwAXyd11ojRXcbEz4BferlrLuXreAOtzdyPBDITuKwT+/NtGkSkT9w/W43EJycXR9xGyNxULspLg84o3Gp1Jg7mwEKLE8cpEA1b+1AH13zsNsewCzIWFNolTiWydO7KOK9TcP919sHzMB/BVGSSNoxWAULW24ndXyN31mrq7YWffOdD1qd5yrp43wPrc3Sjf7I4RC59G5J+2iUupuH+wHo9LSC6Ovo+QvakI9ijC2Thv+KojoT51HqarVyWNp4IKxkAjDIlRcLtcBJzSAWaTjaJVhj/LDPi1sAn2pMei4eVcGCXmzKmp1FAnxMMQ4m05q8wkqeaKzKX4vjgCx3QR8M4UYMq9IjVaxTJ7iKjnJsJH5WPTcV1hfb2eWlAhTGN9Dl2p5m4kNXfldGY9SqINMHdqAbfLRTCdOuuUNSd136oOCICpYSTMHmqLec6at5rat7rado6sZ6vtnFw1J3tT8X8hW/Hj9EvYnNsY+W/Gw23zfknjeQgaLOryJfa1i8W8P7oi4aXLMOUX2ChaZRh9YAzC57qjfq4OpqzLcoejaOoAf5yYUgtP3PebxbzdeXGSau6g3hPvLByMsJ3FiPjnHIxSg3VBrrC+2hJrTjpXqTmp+9aStvUQ8vJZtK6VYTHPWfNWU/tWV6k5Ug65ak72piJK44OJtTIR7nYVs3zj4CZxPLWgQpKXGUleaVgS0QbQyP4n2oZoRnGJB07odSjN9IFm50GYDXq5o1I+lRoBEQWYFphmMSvF45JVNacyiEgtqwODqEbAaSOEXYd5cGclp1xf/7uunjYUI1AFBKstz5JVl86sxyWTHvt1zZy75mogdzdyypqrgtR9q95PjbF1dqCntsxinlPlzQ77VlepObpDTrydY2U7CHNhEcK+9sAjf0xF/LESiEZp17aT9fwOZWHJ7N6ACIQcznTOgzuyWvm6OviPaUCPPOxptQQegrSPSz7PT8DC5T3hc0F06pqridwR3Qr3rWRvzrydY1PhIMSyMnj8uA+hcgdCMKafQ0D6uX//LXMsdsO7PO/Y9etqRvi9MLQ0Sd5h7Loah5hVWTClnnXqmquJ3BHdCvetZG/OvJ1jU0FEt1V7vwlNg54EBInjROVhVZPFiLLBjWiOwFZ58zmrQWR+qm2CchCsOSJyds62nWNTQUS35ZVyAA1/UAMqaVu+gv4tcPKNAERpXOMSA1vlDSYTTEZnPkdhiTVHRM7O2bZzbCqI6PbMJog2eBydygSYIfEb9ByJjfLmklhzROTsnGw7J38ERERERETk0NhUEBERERGRJGwqiIiIiIhIEjYVREREREQkCZsKIiIiIiKShE0FERERERFJwqaCiIiIiIgkYVNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkGrkDKOcumKD3UcEvJKRay+m9BbgJxkrTck3FyDMDJToPQBRtGaYiqLRaCN7eNTK2WFwMs05XI2MrjbU1VxVnz5utas4V11eyDmvONrhvVQbmjuxNjppTTFPR3P0K6j12EicfDq3Wco2DT6Cp+zUA/+58ykQDOh8YD/XmAESkG2AuKq6BaOWV93BzlDycD43KbNNxDSY1vNfEw3/pHpuOq1TW1tyNXCFvtqo5V1xfyTqsOdvgvlV+zB3Zm1w1p5imIkzjg2WxW4BYa5b+36dZpaIR+sMBiP5sFwDAGT8PyE8ADrRaBB+Vp03HLTCXoO2RZ+Fv01GVS1rN/Y8r5M22Neda6ytZhzVnG9y3yo+5I3uTq+YU01RIlW4owuCjY5BzLgBR+423X4AqMHfWYd6sx9yRvbHmrMO8WY+5I3uTu+acpqk4bgiG5psgNFx3EKLJJHc4DoW5sw7zZj3mjuyNNWcd5s16zB3Zm9w1Z9+mwqBH4fFADArsgt7Bf2OEbxbUgrQHUB3Rl+KT7C7YnxWJ4KwyiAa9jYJ1fi6RO9acojB3ZG8uUXPczimKV44eLx5/CKIoMHdkF0qpObs2FaaCa2jwUTqKvqqFGVP6YVC/T6AV3CWN+Ul2F6S9lIDwtFyYs/+BbW9ddm6ukDvWnLIwd2RvrlBz3M4pi9vekwh76t+nbTF3ZA9KqTn7nqkQRRgvZQGXAG1mbaQU14ZWVSZpyP1ZkQhPy4Ux/ZyNglQ+t0IBG4vDmLs7wZqzCdYc2Rtrrhq4nVMUs04HM/NGdqSUmpPtnoqo73Pw6alBECV+/V5wrh7m7H9sE5SDYO6sw7xZj7kje2PNWYd5IyK5yNZUmE6kQnvCNmO52qlF5s46zJv1mDuyN9acdZg3IpKLxM8yiIiIiIjI1bGpICIiIiIiSdhUEBERERGRJGwqiIiIiIhIEjYVREREREQkiSCKoih3EERERERE5Lh4poKIiIiIiCRhU0FERERERJIovqn47LPPIAgC2rZtK3coDmHx4sUQBKHST2hoKO6//35s2rRJ7vAcQlpaGiZMmIC4uDh4enrCz88P7du3x0cffYSSkhK5w1OUG+vN09MT4eHh6NGjBz7++GMUFhbKHaKiVbW+Xv+zZ88euUNUrFvl7j//+Y/c4Slaeno6Jk+ejAYNGkCr1UKr1aJx48aYNGkSjhw5Ind4ilReb/v3769yflJSEpo0aWLnqBzP7fJIVXOUvMn2jdp3aunSpYiJicGff/6JM2fOoF69enKH5BBmzJiB2NhYiKKI7OxsLF68GL1798aGDRvQp08fucNTrB9++AEDBw6Eh4cHRo4ciSZNmkCv12PHjh2YNm0ajh07hgULFsgdpuKU15vBYEBWVha2bt2Kp59+Gu+//z5SUlLQrFkzuUNUtPL83Yjbu9urKnc8uLu5jRs3YvDgwdBoNBg2bBiaN28OlUqFkydPYt26dZg3bx7S09MRHR0td6hE5GAU3VSkp6dj165dWLduHSZMmIClS5di+vTpcoflEHr16oVWrVpV/D5u3DjUrl0by5cvZ1NxE+np6RgyZAiio6Px+++/IywsrGLepEmTcObMGfzwww8yRqhcN9bbCy+8gN9//x19+vRBcnIyTpw4AS8vLxkjVLYb80d3jrm7c2lpaRXbuN9++63SNg4A3n33XXz22WdQqRR/EQMRKZCitxxLly5FQEAAHnjgAQwYMABLly6VOySHVatWLXh5eUGjUXQfKatZs2ahqKgIX375pcXOFvj3U+OnnnpKhsgcU+fOnfHKK6/g3Llz+Pbbb+UOh8jlzZo1C8XFxVi0aFGV2ziNRoMnn3wSkZGRMkRHRI5O8U1F//794e7ujqFDhyI1NRX79u2TOyyHUFBQgNzcXOTk5ODYsWN4/PHHUVRUhOHDh8sdmmJt2LABcXFxaNeundyhOI0RI0YAADZv3ixzJMpWvr5e/3PlyhW5w3IIVeWOqrZx40bUq1eP9yhKUFW95ebmwmAwyB0akewU+7H1gQMHcPLkSXzyyScAgA4dOqBu3bpYunQpWrduLXN0yte1a9dKv3t4eOCrr75Ct27dZIpI2a5du4bMzEw8+OCDcofiVOrWrQt/f3+kpaXJHYqi3bi+Av+us6WlpTJE41iqyh2/fsnStWvXcPHiRTz00EMW8/Lz82E0Git+9/b25uWKN1FVvZVLTEy0YyREyqPYpmLp0qWoXbs27r//fgCAIAgYPHgwvv32W8yZMwdqtVrmCJXt008/RYMGDQAA2dnZ+PbbbzF+/Hj4+vqif//+MkenPNeuXQMA+Pr6yhyJ8/Hx8eFToG7j+vW1HLdxd6aq3JGl8m2cj4+PxbykpCQcPny44vfZs2dj6tSpdovNkdys3p599lmYTCYZIiJSDkU2FSaTCStWrMD999+P9PT0iult27bFnDlz8Ntvv6F79+4yRqh8bdq0qXTz4tChQ9GiRQtMnjwZffr0gbu7u4zRKY+fnx8A8OC3BhQVFSE0NFTuMBTtxvWV7hxzd2fKPzApKiqymDd//nwUFhYiOzubl8jexs3qLSAggJfekctTZFPx+++/49KlS1ixYgVWrFhhMX/p0qVsKqpJpVLh/vvvx0cffYTU1FSepr2Bn58fwsPDcfToUblDcSoXLlxAQUEBH41KJDN/f3+EhYVVuY0rv8ciIyPDzlERkTNR5I3aS5cuRWhoKFavXm3xM3ToUKxfv55fQmaF8mtmq/qkioA+ffogLS0Nu3fvljsUp7FkyRIAQI8ePWSOhIgeeOABnDlzBn/++afcoRCRE1JcU1FSUoJ169ahT58+GDBggMXP5MmTUVhYiJSUFLlDdSgGgwGbN2+Gu7s7GjVqJHc4ivTcc8/B29sb48ePR3Z2tsX8tLQ0fPTRRzJE5ph+//13vPHGG4iNjcWwYcPkDofI5T333HPQarUYO3Zslds43uBORFIo7vKnlJQUFBYWIjk5ucr599xzD0JCQrB06VIMHjzYztE5jk2bNuHkyZMAgMuXL2PZsmVITU3Ff/7zn4r7B6iy+Ph4LFu2DIMHD0ajRo0qfaP2rl27sHr1aowePVruMBWpvN6MRiOys7Px+++/45dffkF0dDRSUlLg6ekpd4iKdv36er127dohLi5OhojIGdWvXx/Lli3D0KFD0bBhw4pv1BZFEenp6Vi2bBlUKhXq1q0rd6hE5IAU11QsXboUnp6eN330qUqlwgMPPIClS5fiypUrCAoKsnOEjuHVV1+t+LenpycSEhIwb948TJgwQcaolC85ORlHjhzB7Nmz8f3332PevHnw8PBAs2bNMGfOHDz66KNyh6hI5fXm7u6OwMBANG3aFB9++CHGjBnDJ2rdgevX1+stWrSITQXZ1IMPPoi///4bc+bMwebNm/HVV19BEARER0fjgQcewMSJE9G8eXO5wySi65SfRVT6UwEFkec7iYiIiIgU6eOPP8ZTTz2FM2fOID4+Xu5wbkpx91QQEREREdG/9u3bB29vb0RHR8sdyi0p7vInIiIiIiJXt3btWmzduhVLly7F+PHjodEo+7Cdlz8RERERESlMbGwsCgsL0a9fP3z44Yfw9vaWO6RbYlNBRERERESS8J4KIiIiIiKShE0FERERERFJcsd3fHRTDazJOBTvF/Nqq5dl7qzLHfPGmrMWa846rDnrseasw5qzHmvOOqw5690udzxTQUREREREkrCpICIiIiIiSdhUEBERERGRJGwqiIiIiIhIEjYVREREREQkCZsKIiIiIiKShE0FERERERFJcsffU2Fr6qBAIDgQUAm2HdgsArl5MF3Js+24CsLcWYd5I3tjzVmPubMO80b2xpqjcrI1FdkDGiJ62Bl4qo02HbfUpME/Sxoi6IvdNh1XSZg76zBvZG+sOesxd9Zh3sjeWHNUTramoigK+CZuA3xUnrYd11yKljHPIMTTE6LRCNFo2yKXk6DRQNBoUBgLfBv3A7Qqd5uO76y5Y94URBAguN8k/yaT0+SuJmtOZ9Yjz6xHt/DnEGSzUZWDubMOt3PWEzQaQK2ucp6o1wOiaOeIHEtNHM8587paIxSyb5WtqagpHoIb2nU5iq21m6DWX+6os+gQzDqd3GFJpvL2xqWxzVHQXI+uTY/ATah6AyiFM+aOeVMW4e7GOPOIL0z+lhs4Z8ldTdbcCb0OfXZMgnuqFyK2ldpsXKVg7qzD7Zz1rs/djdT5GtT/9hrMh47LEJnrcuZ1taYoZd/qdE2Fm6DGoqjtQNR2NAseCmGlF+AEGz5Bq4Wq2xWkt1r53ym232k4Y+6YN2UpjPPB3AcXoae2zGKes+SuJmvurDEQYevd4b12l83GVBLmzjrczlnPMnf/k1KsxazdI+B9yP5xuTJnXldrilL2rU7XVBCRggk2vpHPRWwrBV483R+ZFwPRILNE7nAcCnNH5Bi4rkqgkH0rmwoiIoVbkJUEn1e80ehMGsyFheAV3neOuSNyDFxXHZ9sTYVnjoC3ctpAq7a8jrE6Yj1y8KB3ps1v+HYkReZSfF8cgfSykGot5+q5Y97I3qytuT3psWh4ORfGq1drKDLlY+6sw+0c1TRbHc+5+rrqDGRrKuquOYf9f7aQfMpmRQctYibMRXsX3u4d1HvinYWDEbaretfLuXrumDeyN2trrn5BCUxZl2soKsfA3FmH2zmqabY6nnP1ddUZyNZUGC9kQriQKXkc/4h7UGj2AmB5c4qryDdp4X/WBGHnoWot5+q5Y97I3qytOXPNhONQmDvrcDtHNc1Wx3Ouvq46A5XcARARERERkWNjU0FERERERJKwqSAiIiIiIknYVBARERERkSRsKoiIiIiISBI2FUREREREJAmbCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkrCpICIiIiIiSTSyvfM9zXC5pQ9EiW1NQWMjIjX5ALwAAAbRhNdz7kJKRhOY9gZALLsgOVSli9JcxcXOgF94u2ot56q521YKvHi6PzIvBqJBZonk8Vwib1xfbcLadfVGghkI3VcI/Pm3jSJTPubOOtw/WI81R45KrvVVtqbin+4+WD7mA/iqDJLG0QpAqFpb8XuZaMDKnzqg/nunIZZdgLmwUGqoitfU3Q07+86Brk/1lnPV3C3ISoLPK95odCYN5sJCiBLHc4W8cX21DWvX1Rvlm90xYuHTiPzTNnE5AubOOtw/WI81R45KrvXVvk2FSg11QjwMId4ojdajgZsArcrHtm8BFYyBRhgSo+B2uQg4pQPMJpu+h9KoBRXCNNLz6Oy5+7PMgF8Lm2BPeiwaXs6F8epVSeMVmUvxfXEEjuki4J0pwJR7xUaRKgTXV5uTuq5eX3Pu12wYmANg7qzD/YP1bJU7nVmPkmgDzJ1awO1yEUynzjp+7q7bP1jMKjNBfeo8TBL3sWQ9udZXuzYV6gB/nJhSC0/c9xte0aZBq3K3+Xt4CBos6vIl9rWLxbw/uiLhpcsw5RfY/H2ckbPnbvSBMQif6476uTqYsi5LHu+g3hPvLByMsJ3FiPjnHIw2iFFJuL4qj7PXXE1i7qThumo9Z8zd9fuHG+3Oi0P+m/Fw27xfhsgIkK/m7H6mIiCiANMC02rsLdSCCkleZiR5pWFJRBtAI99tI47GKXMnmlFc4oETeh1KM32g2XkQZoNe0pAqg4jUsjowiGoEnDZC2HXYOQ9QuL4qhs6sxyWTHvt1zZy75v67vp42FCNQBQSrLT8FrS6XyF0N5O1GXFet55S5u8X+IcXjEmb5xsFNhrDoX3LVnBNUNtHNmQuLEPa1Bx75Yyrij5VANEq7JwAA/A5lYcns3oAIhBzOdL4DFFKcz/MTsHB5T/hcEJ265srX18F/TAN65GFPqyXwEKQdmrhC7moib0RE1cWmgpyaWFYGjx/3IdSGYxrTzyEg/dy//7bhuEQ3s+tqHGJWZcGUetapa+769TUj/F4YWpokHxy7Qu5qIm9ERNWlmKYi3VCEwUfHIOd8QLWWqx2Vh1VNFiPKBjdTOSrmjuyNNWcda/Pmc1aDyPzUGopKmWrvN6Fp0JOAIG0cV8udrfLm6usqwO0cUXUppqk4bgiG5psgNPzur2otV9C/BU6+EYAojfTLWhwVc0f2xpqzjrV5g8kEk9FZP2evmlfKATT8QQ2oJB4du1jubJU3V19XAW7niKpLMU2FSVRBZRQhlpVVazmVCTC7+BeDM3dkb6w561ibN5dkNkF09MduysFGeXP1dRXgdo6oulj1REREREQkCZsKIiIiIiKShE0FERERERFJwqaCiIiIiIgkYVNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpJEI3cAOrMel0x6nCmLg8oo2mTMXFMx8sxAic4DEG0zppK5CybofVTwCwmp1nJ6bwFugrHSNGfOnUqrheDtXSNji8XFMOt0NTK2ErHmrGNt3qriajVHd8ZW2zlXXld5XCINt3N3ztnWV9mbis/zE7BweU/4XBARcjgTxtsvcktlogGdD4yHenMAItINMBcV2yROJWvufgX1HjuJkw+HVmu5xsEn0NT9GoB/C9rZc5f3cHOUPJwPjcps03ENJjW818TDf+kem46rZKw561ibtxu5Ys3RnbHVds6V11Uel0jD7dydc7b1VfamYtfVOMSsyoIp9azkFRcASkUj9IcDEP3ZLgCAc38e8K8wjQ+WxW4BYq1Z+n8dsrPnLj8BONBqEXxUnjYdt8BcgrZHnoW/TUdVNtacdaTl7X9csebozth2O+ea6yqPS6Thdu7OOdv6KltT8Xz2XVi1vzV8Ut0QmZ8qebx0QxEGHx2DnHMBiNpvi82A62DurMO8WY+5sw7zRvbmSjXH4xJlYN6sJ3fuZGsqVu1vjUbPnoJZp4PJKP0PP24IhuabIDRcdxCiyWSDCF0Hc2cd5s16zJ11mDeyN1eqOR6XKAPzZj25c2ffpsKgR+HxQAwI6AqfVDeYdTqIEldcrxw9Xjz+EERRQHBWGUSD3kbBKsx/czcosAt6B/+NEb5ZUAvSHt51RF+KT7K7YH9WpHPnzsZcJm+sOeswb+QEXKbmeFxiHW7nFEUpubPrI2VNBdfQ4KN06Mb6IXJRquQVFwDc9p5E2FOlCH+6BJp9p2wQpTKV565obC3M+KUfykTpufskuwvSXkpw+tzZmqvkjTVnHeaNnIGr1ByPS6zD7ZyyKCV39j1TIYowXsqy6ZBmnQ7m9HM2HVORynN3CdBm1kZKcW1oVWWShtyfFYnwtFwYXSF/ANwKBWwsDmPe7hRrzjrMG8mI27lq4nGJdbidswlnW19lf/oTVV/U9zn49NQgiBLPMwXn6mHO/sc2QTkA5s16zJ11mDeyN9Yc2RtrznrOljs2FQ7IdCIV2hO2Gcu239igbMyb9Zg76zBvZG+sObI31pz1nC13dr2ngoiIiIiInA+bCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkgiiKIpyB0FERERERI6LZyqIiIiIiEgSxTYVixcvhiAI2L9/f6XpBQUFaNOmDTw9PfHTTz/JFJ2y3Sx3VLXyfAmCgB07dljMF0URkZGREAQBffr0kSFCZSvPn6enJzIzMy3mJyUloUmTJjJE5hiOHTuG4cOHIyIiAh4eHggPD8ewYcNw7NgxuUNTtOvX2/L6a9CgASZPnozs7Gy5w1O0qmpu+PDhOH78uNyhKdaN9abRaBAREYHRo0dXud2jyv7++28MGDAA0dHR8PT0REREBLp164ZPPvlE7tAU6fpau9XP1q1b5Q61Eof68rtr166he/fuOHLkCNavX4+ePXvKHRI5EU9PTyxbtgwdOnSoNP2PP/7AhQsX4OHhIVNkjqGsrAzvvPMOdxLVsG7dOgwdOhSBgYEYN24cYmNjkZGRgS+//BJr1qzBihUr0K9fP7nDVLQZM2YgNjYWpaWl2LFjB+bNm4cff/wRR48ehVarlTs8xbldza1cuRIPPvig3GEq1vX1tmfPHixevBg7duzA0aNH4enpKXd4irRr1y7cf//9iIqKwqOPPoo6derg/Pnz2LNnDz766CNMmTJF7hAVZ8mSJZV+/+abb/DLL79YTG/UqJE9w7o9UaEWLVokAhD37dsniqIoXrt2TbznnntEd3d3cePGjTJHp2w35o5urTxf/fv3F4ODg0WDwVBp/qOPPiq2bNlSjI6OFh944AGZolSu8vzdddddooeHh5iZmVlpfqdOncTExESZolOuM2fOiFqtVkxISBAvX75caV5OTo6YkJAgent7i2lpaTJFqGw328793//9nwhAXLZsmUyRKded1JyPj4949uxZmSJUrpvV2/PPPy8CEFeuXClTZMrXu3dvMSQkRLx69arFvOzsbPsH5IAmTZokKviQvYJiL3+6XlFREXr27ImDBw9i7dq1eOCBB+QOiZzQ0KFDceXKFfzyyy8V0/R6PdasWYNHHnlExsgcw4svvgiTyYR33nlH7lAcwuzZs6HT6bBgwQKEhIRUmhccHIz58+ejuLgYs2bNkilCx9S5c2cAQHp6usyRKM+d1FxRURFmz54tU4SO57777gMApKWlyRyJcqWlpSExMRG1atWymBcaGmr/gKjGKL6pKC4uRq9evbBv3z6sXr2a17RTjYmJicG9996L5cuXV0zbtGkTCgoKMGTIEBkjcwyxsbEYOXIkvvjiC1y8eFHucBRvw4YNiImJqTgouVHHjh0RExODH374wc6RObbyg7ugoCCZI1GeO625DRs22Dkyx5WRkQEACAgIkDcQBYuOjsaBAwdw9OhRuUOhGqb4pmLUqFHYu3cvVq9ejeTkZLnDISf3yCOP4LvvvkNJSQkAYOnSpejUqRPCw8NljswxvPTSSzAajXj33XflDkXRCgoKcPHiRTRv3vyWr2vWrBkuXLiAwsJCO0XmeAoKCpCbm4sLFy5g5cqVmDFjBry8vPgB1A1Yc7Zxfb2tXbsWr7/+Ojw8PFhvtzB16lTodDrcddddaNeuHZ5//nls3rwZBoNB7tDIxhTfVGRnZ8PT0xORkZFyh0IuYNCgQSgpKcHGjRtRWFiIjRs38tKnaoiLi8OIESOwYMECXLp0Se5wFKv8gM3X1/eWryuff+3atRqPyVF17doVISEhiIyMxJAhQ+Dj44P169cjIiJC7tAUpbo1x6aiatfX24ABA+Dt7Y2UlBTUrVtX7tAUq1u3bti9ezeSk5Nx+PBhzJo1Cz169EBERARSUlLkDo9sSPFNxfz58+Hu7o6ePXvi1KlTcodDTi4kJARdu3bFsmXLsG7dOphMJgwYMEDusBzKyy+/DKPRyHsrbuFOD9zu9EDQlX366af45ZdfsGXLFhw/fhxnz55Fjx495A5LcapTc4IgIDg42B5hOZzyeluzZg169+6N3NxcPhnwDrRu3Rrr1q3D1atX8eeff+KFF15AYWEhBgwYwEcZOxHFNxWNGzfGjz/+iJKSEnTr1g3nz5+XOyRyco888gg2bdqEzz//HL169ary5jK6ubi4OAwfPpxnK27B398fYWFhOHLkyC1fd+TIEURERMDPz89OkTmeNm3aoGvXrkhKSkKjRo2gUil+tyYLf39/hIeH31HN1a1bF+7u7naKzLGU19vDDz+MlJQUNGnSBI888giKiorkDs0huLu7o3Xr1njrrbcwb948GAwGrF69Wu6wyEYcYuvbpk0bfPfdd7h8+TK6deuGnJwcuUMiJ9avXz+oVCrs2bOHlz5ZqfxsBe+tuLk+ffogPT29yi9cBIDt27cjIyOD12qTzfTt2/eOam7gwIF2jswxqdVqvP3227h48SLmzp0rdzgOp1WrVgDAD5+ciEM0FQDQpUsXLF++HGfOnEHPnj15jTHVGB8fH8ybNw+vvfYa+vbtK3c4Dik+Ph7Dhw/H/PnzkZWVJXc4ijRt2jR4eXlhwoQJuHLlSqV5eXl5mDhxIrRaLaZNmyZThORspk6dCq1We8ua8/Pzw+TJk2WK0PEkJSWhTZs2+PDDD1FaWip3OIq0ZcsWiKJoMf3HH38EADRs2NDeIVENcahv1O7Xrx+++OILjB07FsnJyfjpp5/4DZa38NVXX+Gnn36ymP7UU0/xGu3bGDVqlNwhOLyXXnoJS5YswalTp5CYmCh3OIpTv359fP311xg2bBiaNm1q8e3Gubm5WL58OeLj4+UOlZxEvXr18M0332Do0KFV1tzVq1exYsUKxMbGyh2qQ5k2bRoGDhyIxYsXY+LEiXKHozhTpkyBTqdDv379kJCQAL1ej127dmHlypWIiYnBmDFj5A6RbMShmgoAGDNmDPLy8jB16lQMHDgQ69evh0bjcH+GXcybN6/K6aNHj2ZTQTWuXr16GD58OL7++mu5Q1GsgQMHIiEhAW+//XZFIxEUFIT7778fL774Ipo0aSJ3iORkHn74YRw8eBBvv/02Fi5ciMuXL8NsNsPT0xMHDhxA48aN5Q7R4fTv3x/x8fF477338Oijj0KtVssdkqK89957WL16NX788UcsWLAAer0eUVFReOKJJ/Dyyy/zvkUnIohVnZMiIiIil/DNN99g9OjRGD58OL755hu5wyEiB8WP+ImIiFzYyJEjcenSJfznP/9B3bp18dZbb8kdEhE5IJ6pICIiIiIiSRzm6U9ERERERKRMbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpKETQUREREREUlyx4+U7aYaWJNxKN4v5tVWL8vcWZc75o01Zy3WnHVYc9ZjzVmHNWc91px1WHPWu13ueKaCiIiIiIgkYVNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCTJHT9SlohclzooEAgOBFSCbQc2i0BuHkxX8mw7rkIwb9Zj7qzDvFmPubMO80bl2FQQ0W1lD2iI6GFn4Kk22nTcUpMG/yxpiKAvdtt0XKVg3qzH3FmHebMec2cd5o3K2b2pEDQaQK2ucp6o1wOiaOeIHAdzR/YmaDQQNBoUxgLfxv0ArcrdpuMXmUvRMuYZhHh6QjQaIRptu1OSC/NmPebOOsyb9WoydzqzHnlmPbqFP4cgm42qDMybgggCBPeb5N9kstv6atemQuXtjUtjm6Ogud5injpfg/rfXoP50HF7huQwmDuyt+trrmvTI3ATqm5opfAQ3NCuy1Fsrd0Etf5yR51Fh2DW6Wz+PvbEvFmPubMO82a9mszdCb0OfXZMgnuqFyK2ldpsXCVg3pRFuLsxzjziC5O/ZfNgz/XVrk2FoNVC1e0K0luttJiXUqzFrN0j4H3InhE5DuaO7M2y5mx/oOImqLEoajsQtR3NgodCWOkFOPiBCvNmPebOOsyb9Woyd2eNgQhb7w7vtbtsNqZSMG/KUhjng7kPLkJPbZnFPHuur7yngoiIiMhGtpUCL57uj8yLgWiQWSJ3OA6DeZNAsPFN8lZiU0FERERkIwuykuDzijcanUmDubAQvNvxzjBvjo9NBRFVW5G5FN8XRyC9LKRay8V65OBB70z4qDxrKDJlY96sx9xZh3mznrW525Mei4aXc2G8erWGIlM25s11sakgomo7qPfEOwsHI2xX9a7RXNFBi5gJc9HeRY9TmDfrMXfWYd6sZ23u6heUwJR1uYaiUj7mzXWxqSCiass3aeF/1gRh56FqLecfcQ8KzV4ALG8mcwXMm/WYO+swb9azNnfmmgnHYTBvrksldwBEREREROTY2FQQEREREZEkbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpKETQUREREREUnCpoKIiIiIiCRhU0FERERERJKwqSAiIiIiIknYVBARERERkSRsKoiIiIiISBKN3AFsKwVePN0fmRcD0SCzRO5wHApzR/Zm65oziCa8nnMXUjKawLQ3AGLZBRtEqTzMm3RRmqu42BnwC29XreUKGhsRqckH4AXA9XLHvFUf963SWFtzNxLMQOi+QuDPv20UmQLd0wyXW/pAlPgRv1LWV9mbigVZSfB5xRuNzqTBXFgIUe6AHAhzR/Zm65orEw1Y+VMH1H/vNMSyCzAXFtokTqVh3qRr6u6GnX3nQNenestpBSBUra343dVyx7xVH/et0lhbczfKN7tjxMKnEfmnbeJSon+6+2D5mA/gqzJIGkcp66tsTcWfZQb8WtgEe9Jj0fByLoxXr8oVisMK9ijC2Thv+KojoT51HibmkGqYrWtOBRWMgUYYEqPgdrkIOKUDzCYbRasczJt0akGFMI2P1csXmUvxfXEEjuki4J0pwJR7xYbRKZfUvJVzpZrjvlUaW9WczqxHSbQB5k4t4Ha5CKZTZ52j5lRqqBPiYQjxRmm0Hg3cBGhV0vMFyL+dk+2eitEHxuD3J9uj/tslMGVdlisMh/Z/IVsxcvoGeM+6hNLW8XKHQy7A1jXnIWiwqMuX6P7pNpycHAi1n202rErDvMnvoN4T7ywcjP2TWiBi/Tm5w3E4rlRz3Lcqg7PWnDrAHyem1EL3T7fh6/sXQqtyt9nYcm/n7NtUiGYUl3jghF6H0kwfaHYehfnoSYhlZXYNw1lEaXwwsVYmRofthN5XLXc45AJsXXNqQYUkLzOmBabBJ+IaoJH9iswawbzJR2fWI81QhP26OAScNkLYdRjGC5lyh2Vb/923njYUI9dUXCNv4Uo1x33rHWDNWU+lRkBEAaYFpqGjp22GVMp2zq7/h8yFRQj72gOP/DEV8cdKIBqlXUNGRER0K5/nJ2Dh8p7wuSAi5HAmjHIHVAPK962D/5gG9MjDnlZL4CG4yR0WOTHWnLIoZTtn16ZCLCuDx4/7EGrPNyUiIpe162ocYlZlwZR61ikbCqDyvjUj/F4YWpp4gEc1ijWnLErZzjnJuSQiInJm6YYiDD46BjnnA6q1nM9ZDSLzU2soKuWpvd+EpkFPAoLEcaLysKrJYkTZ4IZbcm6sOdtx9O0cmwoiIlK844ZgaL4JQsPv/qregiYTTEZnPUdhySvlABr+oAZU0o7wCvq3wMk3AhCl4WXKdGusOdtx9O0cmwoiIlI8k6iCyijywR63YzZBtMFjN1UmwCzfAyLJkbDmbMbRt3Ou/X+PiIiIiIgkY1NBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpKETQUREREREUnCpoKIiIiIiCRhU0FERERERJJo5A6ApHMXTND7qOAXEiJ5LLG4GGadzgZRkTPSmfW4ZNLjTFkcVEbRJmPmmoqRZwZKdB6AaJsxlcradVXvLcBNMFaa5ip5q4maI+uw5sjeXKXmyjn68RybCifQ3P0K6j12EicfDpU0jsGkhveaePgv3WOjyMjZfJ6fgIXLe8LngoiQw5kw3n6RWyoTDeh8YDzUmwMQkW6AuajYJnEqlbXrauPgE2jqfg2ANwDXyputa46sw5oje3Olmivn6MdzbCqcQJjGB8titwCx0sYpMJeg7ZFn4W+bsMgJ7boah5hVWTClnrXJjrZUNEJ/OADRn+0CADj751DS1lXvin+5Ut5sXXNkHdYc2Zsr1Vw5Rz+eY1NBSDcUYfDRMcg5F4Co/dyEkqXns+/Cqv2t4ZPqhsj8VMnjseas40p5s3XNkXVYc2RvrlRztiZ37thUEI4bgqH5JggN1x2EaDLJHQ4p0Kr9rdHo2VMw63QwGaVvqFhz1nGlvNm65sg6rDmyN1eqOVuTO3dsKhyFQY/C44EYFNgFvYP/xgjfLKgFaQ/vOqIvxSfZXbA/KxLBWWUQDXobBUtO4b81NyCgK3xS3WDW6SBK3NF65ejx4vGHIIqC89Yc11Xr1UDNkXVcYl0FWHMK4mo154z7CDYVDsJUcA0NPkpH0Ve1MGNKPwzq9wm0grukMT/J7oK0lxIQnpYLc/Y/MNsoVnIO5TWn+9IPkfmpNvnkzm3vSYQ99e9TLZy15riuWq8mao6s4wrrKsCaUxJXqzln3EewqXAUogjjpSzgEqDNrI2U4trQqsokDbk/KxLhabkwpp+zUZDkVMprzobMOh3Mzl5vXFetVwM1R9ZxiXUVYM0piMvVnBPuI9hUOKCo73Pw6alBECV+dWFwrh7m7H9sExQRWeC6SkREN+Ns+wg2FQ7IdCIV2hO2GctZTy8SKQHXVSIiuhln20dI7I2IiIiIiMjVsakgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCSJIIqiKHcQRERERETkuHimgoiIiIiIJFFkU/Haa69BEATk5uZWOb9JkyZISkqyb1AOIDk5GVqtFoWFhTd9zbBhw+Du7o4rV67YMTJlWrVqFQRBwPr16y3mNW/eHIIgYMuWLRbzoqKi0K5dO3uE6BCGDx8OT09PnD592mLeO++8A0EQsHHjRhkiU75evXohICAA2dnZFvMKCgoQFhaGtm3bwmxWwhPIlUMQhNv+vPbaa3KHSUTkUhTZVJB1hg0bhpKSkioPkgFAp9Ph+++/R8+ePREUFGTn6JSnQ4cOAIAdO3ZUmn7t2jUcPXoUGo0GO3furDTv/PnzOH/+fMWyBLz//vvQarWYOHFipenp6emYMWMGHn74YfTp00em6JTts88+g16vxzPPPGMx78UXX0Rubi4WLFgAlYqb6ustWbLkpj/x8fEAgLZt28ocJRGRa+E3ajuR5ORk+Pr6YtmyZRg5cqTF/O+//x7FxcUYNmyYDNEpT3h4OGJjYy2ait27d0MURQwcONBiXvnvbCr+JzQ0FO+++y4ee+wxfP311xg1ahQA4IknnoCbmxs++ugjmSNUrtjYWEyfPh3PP/88Ro8eje7duwMA9u3bh88//xxTp05F8+bNZY5SeYYPH17l9IULFyItLQ1TpkxBr1697BwVEZFr48dfTsTLywv9+/fHb7/9hsuXL1vMX7ZsGXx9fZGcnCxDdMrUoUMH/PXXXygpKamYtnPnTiQmJqJXr17Ys2dPpUtPdu7cCUEQ0L59eznCVazx48ejffv2mDp1Kq5cuYIVK1bgp59+wptvvomIiAi5w1O0//u//0OzZs3wxBNPoLS0FCaTCRMnTkR0dDSmT58ud3gO49ixY3jyySfRokULzJ49W+5wiIhcDpsKJzNs2DAYjUasWrWq0vS8vDz8/PPP6NevH7y8vGSKTnk6dOgAg8GAvXv3VkzbuXMn2rVrh3bt2qGgoABHjx6tNC8hIYGXj91AEATMnz8fBQUFePzxx/HMM8+gVatWmDRpktyhKZ5Go8GCBQuQnp6ON954A3PnzsXBgwcxb948aLVaucNzCDqdDoMGDYJarcaKFSvg4eEhd0hERC6HTYWT6dy5M8LCwrBs2bJK01evXg2DwcBLn25w430VRqMRe/fuRfv27REfH4/atWtXzCssLMTff//NS59uIjExEVOnTsXq1auRk5OD+fPn816AO9S2bVs88cQTmD17Nl5++WUMHToUPXr0kDsshzFlyhQcP34cn376KRo0aCB3OERELol7fCejVqsxZMgQ7N69GxkZGRXTly1bhtq1a6NLly7yBadAjRo1QlBQUEXjcPjwYRQXF1c83aldu3YVN2vv3r0bJpOJTcUtBAcHA/j3fpUmTZrIHI1jmTlzJoKCgqBSqfDBBx/IHY7DWLZsGb766iuMGDGiynvJiIjIPhy2qRAEQe4QFKv8bET52YoLFy5g+/btGDJkCNRqtZyhKY4gCGjXrl3FvRM7d+5EaGgo6tWrB6ByU1H+XzYVVTt//jymT5+OJk2a4Pz585g1a5bcITkUPz8/NGzYEJGRkahdu7bc4TiE1NRUTJw4EQ0aNMBnn30mdzhERC5NkU2Fp6cnAFS6efZ6Op2u4jVkqWXLlkhISMDy5csBAMuXL4coirz06SY6dOiAgoIC/P333xX3U5Rr164dzp07h8zMTOzYsQPh4eGIi4uTMVrlmjx5MgBg06ZNGDhwIGbOnImzZ8/KHBU5q7KyMgwePBh6vR4rVqyAj4+P3CEREbk0RTYV0dHRAIBTp05ZzNPpdDh//nzFa6hqw4YNw9GjR3HkyBEsW7YM9evXR+vWreUOS5Guv69i586dlZ7s1LJlS3h4eGDr1q0V91qQpfXr1yMlJQVvvPEG6tatiw8//BDu7u68UZtqzNSpU/HXX39h1qxZaNGihdzhEBG5PEU2FV26dIG7uzvmzZtn8U2yCxYsgNFo5DPIb6P8rMSrr76KQ4cO8SzFLbRq1Qqenp5YunQpMjMzK52p8PDwwN13341PP/0UxcXFvPSpCoWFhRWP8pwyZQqAf++peOONN/DTTz9h9erVMkdIzmb9+vWYO3cukpOT8eSTT8odDhERARBEURTlDqIqM2fOxMsvv4z27dsjOTkZWq0Wu3btwvLly9G9e3ds2rSJT5a5jfbt22PXrl0A/r32uPw+AbLUsWNHbN++HR4eHigoKKj0SMqpU6dizpw5AIADBw7g7rvvlitMRXrqqacwd+5c7Nmzp9LZMJPJhDZt2iArKwsnT56Er6+vjFE6hqSkJOTm5lZ6jDFVdunSJSQmJuLatWt4//33ERgYWOXr4uPjce+999o5OiIi16XYb9R+6aWXEBMTg7lz52LGjBkwGo2IjY3F66+/jueff54NxR0YNmwYdu3ahTZt2rChuI0OHTpg+/btFZc7Xa99+/aYM2cOfH19+e3GNzhw4AA+/fRTPPHEExaX16nVanz++ee455578PLLL/ObtckmTp06hatXrwL4t6G9mVGjRrGpICKyI8WeqSAiIiIiIsfAj/uJiIiIiEgSNhVERERERCQJmwoiIiIiIpKETQUREREREUnCpoKIiIiIiCRhU0FERERERJKwqSAiIiIiIknu+MvvuqkG1mQciveLebXVyzJ31uWOeWPNWYs1Zx3WnPWk5I6IyBnwTAUREREREUnCpoKIiIiIiCRhU0FERERERJKwqSAiIiIiIknYVBARERERkSRsKoiIiIiISJI7fqQsERGRvaiDAoHgQEAl2HZgswjk5sF0Jc+24xIRuTg2FUREpDjZAxoietgZeKqNNh231KTBP0saIuiL3TYdl4jI1dm9qRA0GkCtrnKeqNcDoljNAQUI7u5VzzOZIBptu0OSE3NH5Bi4rkpXFAV8E7cBPipP245rLkXLmGcQ4ukJ0Wh0ytwREcnBrk2Fytsbl8Y2R0FzvcU8db4G9b+9BvOh49UaU7i7Mc484guTv+WOodZf7qiz6BDMOp3VMSsFc0fkGLiuKpuH4IZ2XY5ia+0mzB0RkQ3ZtakQtFqoul1BequVFvNSirWYtXsEvA9Vb8zCOB/MfXARemrLLOY1Cx4KYaUX4AQ7DOaOyDFwXVU2N0GNRVHbgajtzB0RkQ05/tOfBBvfxOdKmDsix8B1lYiIFM7xmwoiIiIiIpIVn/5ERESK45kj4K2cNtCqLe9NqY5Yjxw86J1p8xu+iYioMjYVRESkOHXXnMP+P1tIvvRrRQctYibMRXv2FERENYpNBRERKY7xQiaEC5mSx/GPuAeFZi8Alje5ExGR7fCeCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkrCpICIiIiIiSdhUEBERERGRJGwqiIiIiIhIEjYVREREREQkCZsKIiIiIiKShE0FERERERFJwqaCiIiIiIgk0cgdwLZS4MXT/ZF5MRANMkskj2cQTXg95y6kZDSBaW8AxLILNohSmZg7IscSpbmKi50Bv/B21VquoLERkZp8AF4AXGRdvacZLrf0gSjxoy+XzB0RkQxkbyoWZCXB5xVvNDqTBnNhIUSJ45WJBqz8qQPqv3caYtkFmAsLbRKnEjF3RI6lqbsbdvadA12f6i2nFYBQtbbid1dYV//p7oPlYz6Ar8ogaRxXzB0RkRxkbyqCPYpwNs4bvupIqE+dh+nqVUnjqaCCMdAIQ2IU3C4XAad0gNlko2iVxVa5KzKX4vviCBzTRcA7U4Ap94qNIyUiAFALKoRpfCSP47TbOZUa6oR4GEK8URqtRwM3AVqV9HwB3M4REdU02e+p+L+QrRg5fQO8Z11Caet4yeN5CBos6vIlun+6DScnB0LtZ5sdkhLZKncH9Z54Z+Fg7J/UAhHrz9kwQiKqCc66nVMH+OPElFro/uk2fH3/QmhV7jYbm9s5IqKaJfuZiiiNDybWykS421XM8o2Dm8Tx1IIKSV5mJHmlYUlEG0Aj+59YY6TmTmUQkVpWBwZRjYDTRgi7DsNYI5ESuRDRjOISD5w2FCNQBQSrvW3+Fk67nVOpERBRgGmBaTYbUmfW45JJj/26ZtzOERHVICfZE5E1/A5lYcns3oAIhBzO5I6WyAbMhUUI+9oDg/+YBvTIw55WS+AhSP24hKz1eX4CFi7vCZ8LIrdzREQ1iE2FCzOmn0NA+r+XAXBHS2QbYlkZPH7ch1AAGeH3wtDSxKZCRruuxiFmVRZMqWe5nSMiqkFsKoiIakjt/SY0DXoSECSOE5WHVU0WI8oGN3k7qnRDEQYfHYOc8wHVWs7nrAaR+ak1FBUREZVjU0FEVEO8Ug6g4Q9qQCWtqyjo3wIn3whAlEba41Ud2XFDMDTfBKHhd39Vb0GTCSYjz1EQEdU0NhVERDXFbIJog0e9qkyAWf6H9cnKJKqgMooQy8rkDoWIiKrg2nspIiIiIiKSjE0FERERERFJwqaCiIiIiIgkYVNBRERERESSsKkgIiIiIiJJ2FQQEREREZEkbCqIiIiIiEgSNhVERERERCQJmwoiIiIiIpKETQUREREREUmikTuAcu6CCXofFfxCQqq1nN5bgJtgrDQt11SMPDNQovMARNGWYSqKzqzHJZMeZ8rioDI6799J5KhUWi0Eb2/J47jydq6ctfuIqojFxTDrdDaIioiIyimmqWjufgX1HjuJkw+HVmu5xsEn0NT9GoB/d9xlogGdD4yHenMAItINMBcV10C0yvB5fgIWLu8JnwsiQg5nwnj7RYjIjvIebo6Sh/OhUZkljePK27ly1u4jbmQwqeG9Jh7+S/fYKDIiIgIU1FSEaXywLHYLEGvN0v/7JLBUNEJ/OADRn+0CADjz53e7rsYhZlUWTKln2VAQKVB+AnCg1SL4qDxtMJprbufKSdtH/E+BuQRtjzwLf9uERURE/6WYpkKqdEMRBh8dg5xzAYja79yH2M9n34VV+1vDJ9UNkfmpcodDRHbiSts5W2PuiIhqltM0FccNwdB8E4SG6w5CNJnkDqdGrdrfGo2ePQWzTgeTkTtHIlfhSts5W2PuiIhqln2bCoMehccDMSiwC3oH/40RvllQC9IeQHVEX4pPsrtgf1YkgrPKIBr0NgpWYf6buwEBXeGT6gazTgeRDQWRS3C17Rz3EUREjseuj5Q1FVxDg4/SUTS2Fmb80g9lovSD4k+yuyDtpQSEP10Czb5TNohSmcpzpxvrh8hFqWwoiFyIq23nuI8gInI89j1TIYowXsoCLgHazNpIKa4NrapM0pD7syIRnpYLY/o5GwWpUOW5IyKH4VYoYGNxGLdzd4r7CCIihyXbPRVR3+fg01ODIEo8VxKcq4c5+x/bBEVEZEPczlmPuSMiciyyNRWmE6nQnrDNWNKeAE9EVDO4nbMec0dE5Fjsek8FERERERE5HzYVREREREQkCZsKIiIiIiKShE0FERERERFJwqaCiIiIiIgkEURRFOUOgoiIiIiIHBfPVBARERERkSRsKoiIiIiISBI2FUREREREJAmbCiIiIiIikoRNBRERERERScKmgoiIiIiIJGFTQUREREREkrCpICIiIiIiSdhUEBERERGRJP8P63g1J4wl+0oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 349 ms, sys: 15.4 ms, total: 365 ms\n",
      "Wall time: 316 ms\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 60.8 ms, sys: 4.81 ms, total: 65.6 ms\n",
      "Wall time: 19 ms\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in range(4):\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    if t == 0:\n",
    "        %time S = literal_speaker()\n",
    "        plt.suptitle('Literal speaker')\n",
    "    else:\n",
    "        %time S = speaker(10.0, t - 1)\n",
    "        plt.suptitle(f'Strategic level {t} speaker')\n",
    "    for i in range(36):\n",
    "        plt.subplot(4, 10, i + 1)\n",
    "        plt.imshow(make_7seg(jnp.argmax(S[i])))\n",
    "        plt.axis('off')\n",
    "        plt.title(chars[i])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbafd000-9752-494f-a86e-28bd1f4deaba",
   "metadata": {},
   "source": [
    "This demo isn't perfect, but you can see that the strategic speakers are \"trying\" to minimize ambiguity, and often come up with some pretty \"clever\" solutions as well (I particularly like the \"V\").\n",
    "\n",
    "By the way, notice the timestamps: memo evaluations are just milliseconds."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
